> [!NOTE] > The OpenGCM naming and the models are now deprecated. We recommend switching to Polaris. > OpenGCM-v2 is a reasoning-focused 9B parameter model developed by NitrAI. The model is built on top of the next-generation Qwen3.5-9B base model, which features state-of-the-art architectures and a 262k context window. The goal of OpenGCM-v2 is to distill complex coding-agent trajectories, multi-step math logic, and system-level reasoning from frontier LLMs (GPT-5.5, Claude-Fable-5, and GLM-5.2) into a highly efficient, lightweight consumer-hardware-friendly model. To prevent VRAM paging bottlenecks during training on consumer GPUs, the dataset was strictly audited, cleaned of outlier long sequences, and downsampled to fit an optimal token budget. The final fine-tuning dataset consists of 597 high-signal QA items containing 904,466 tokens in total. The training was performed locally on a single consumer GPU setup using the Unsloth library (leveraging optimized Triton fused kernels for training acceleration) and DoRA (Weight-Decomposed Low-Rank Adaptation). Base Model: Qwen/Qwen3.5-9B PEFT Method: DoRA (Weight-Decomposed LoRA) Rank (r): 64 Alpha (α): 128 Target Modules: qproj,…
Модальности:
Генерация текста
Области применения:
Логика и рассуждение Диалог / чат
Задача: Генерация текста
Автор: nitrai-research
Теги: qwen3_5, reasoning, agent-traces, distillation, dora, qwen, nitrai, opengcm
Лайков: 3 | Загрузок: 48
Описание основано на материалах HuggingFace. Перевод выполнен автоматически.
